Twin least squares support vector regression

Neurocomputing

Volumn 118, Issue , 2013, Pages 225-236

  Zhao, Yong Ping ^a   Zhao, Jing ^a   Zhao, Min ^b  

^a NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b Bureau of Science and Technology   (China)

Author keywords

Kernel method; Least squares; Support vector machine; Support vector regression

Indexed keywords

COMPUTATIONAL SPEED; EFFECTIVENESS AND EFFICIENCIES; GENERALIZATION PERFORMANCE; KERNEL METHODS; LARGE SCALE DATA SETS; LEAST SQUARE; LEAST SQUARES SUPPORT VECTOR REGRESSION; SUPPORT VECTOR REGRESSION (SVR);

ALGORITHMS; GEOMETRY; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES; VECTORS;

LEAST SQUARES APPROXIMATIONS;

ALGORITHM; ARTICLE; COMPUTER SYSTEM; FEASIBILITY STUDY; FURNACE; INTERMETHOD COMPARISON; KERNEL METHOD; LINEAR SYSTEM; MATHEMATICAL MODEL; PREDICTIVE VALUE; PRIORITY JOURNAL; PROCESS OPTIMIZATION; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINE; SYSTEMATIC ERROR; TWIN LEAST SQUARES SUPPORT VECTOR REGRESSION; VALIDATION PROCESS;

EID: 84881257396     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2013.03.005     Document Type: Article

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